Computing system for auto-identification of secondary insights using reverse extraction

ABSTRACT

A computing system obtains a first article about a first topic, where the first article references a plurality of entities including a person. The computing system identifies the person as a dominant entity of the first article. The computing system determines whether the first article expresses negative sentiment based upon content of the first article. In accordance with a determination that the first article does not express negative sentiment, the computing system retrieves a uniform resource locator (URL) of a webpage about a second topic that is of interest to the first person. Upon receiving a request for the first article from a computing device, the computing system causes the first article and a link to be concurrently displayed on a display, where the URL of the webpage is embedded in the link.

RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/226,364, filed on Jul. 28, 2021, and entitled “COMPUTING SYSTEMFOR AUTO-IDENTIFICATION OF SECONDARY INSIGHTS USING REVERSE EXTRACTION”,the entirety of which is incorporated herein by reference.

BACKGROUND

A news application presents titles of articles to users on a display ofa computing device. A title of the article is selected by a user and thearticle is then presented on the display. The user may select an articlebased upon an interest of the user in a person referenced in thearticle, such as when the person referenced in the article is a famousactor, politician, athlete, etc. When the user is particularlyinterested in the person and wishes to obtain additional informationabout the person that is not found in the article, the computing devicereceives input from the user that causes a search engine page to beopened on the computing device. The computing device receives a searchquery as input from the user and transmits the search query to thesearch engine. The computing device receives search results from thesearch engine that include the additional information about the person.The computing device presents the search results to the user.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Various technologies pertaining to identifying a topic that is ofinterest to a person referenced in an article (e.g., a secondaryinsight) are described herein. For example, some users visit newsplatforms to read articles about their favorite celebrities (e.g.,television stars, politicians, authors, etc.). These users may also beinterested in following interests of their favorite celebrities. Asdescribed herein, the discoverability of celebrity interests for usersis improved by providing one or more links to webpages that includeinformation about interests of a celebrity. When a link is selected, awebpage with information about an interest of a celebrity is presentedto the user. In an example where an actor is interested in tennis or atennis player, the webpage can be a vertical landing page for tennis orthe tennis player. As discussed in detail below, the present disclosuredescribes generating secondary insights for any article using sentimentanalysis along with dominant entity analysis and a knowledge graph builtfor insights.

In an example, a computing system determines a first topic (e.g., aperson, place, thing, idea, or an event) that is of interest to a person(e.g., an actor) based upon an entry for the person in a knowledge graphand a first article that references both the first topic and the person.When a request for a second article about a second topic is receivedfrom a computing device, the computing system determines whether theperson is a dominant entity in the second article and whether the secondarticle expresses non-negative sentiment. When the person is a dominantentity and when the second article expresses non-negative sentiment, thecomputing system causes the second article and a link to be presented ona display. When the link is selected, a webpage that includesinformation about the first topic is presented on the display.

In another example, a computing system executes a search over aknowledge graph based upon an identifier for a profession (e.g., actor).The search produces search results, where the search results include anentry in the knowledge graph for a person that belongs to the profession(e.g., the person is an actor). The computing system determines a nameof the person based upon the entry for the person in the knowledgegraph. The computing system retrieves (e.g., identifies) a plurality ofarticles (e.g., news articles) based upon the name of the person. Thecomputing system determines a first topic (e.g., a sports team) that isof interest to the person based upon content of at least one of theplurality of articles. The computing system retrieves a uniform resourcelocator (URL) of a webpage that includes information about the firsttopic and stores the URL, a name of the person, and a name of the firsttopic in computer-readable storage.

The computing system obtains an article (e.g., a news article), wherethe article references a plurality of entities including the person, andwhere the article is about a second topic. The computing systemdetermines that the person is a dominant entity of the article basedupon a number of references to the person within the article. Thecomputing system determines whether the article expresses non-negativesentiment based upon content of the article. When the article expressesnon-negative sentiment (e.g. positive sentiment or neutral sentiment),the computing system retrieves the URL of the webpage that includesinformation about the first topic that is of interest to the personbased upon the name of the person in the article. Upon receiving arequest for the article from a computing device, the computing systemcauses the article and a link to be presented concurrently on a display,where the URL is embedded within the link. According to embodiments, thelink is in the form of a question that includes the name of the personand the name of the first topic. When the link is selected, the webpagethat includes information about the first topic is presented on thedisplay.

The above-described technologies present various advantages overconventional technologies for discovering information about interests ofa person. First, by leveraging information within a knowledge graph, theabove-described technologies enable interests of a wide variety ofpeople of varying professions to be discovered. Second, theabove-described technologies ensure that a link with additionalinformation about an interest of a person is inserted into an articlewhen the article is primarily directed towards the person (e.g., theperson is the dominant entity of the article). This can save user timeand effort and can save computing and network resources by avoidingsearch engine procedures. Third, via sentiment analysis, theabove-described technologies ensure that a link with additionalinformation about a person is not inserted into an article in aninappropriate circumstance, such as when the article pertains to atragic event. Fourth, unlike conventional technologies, theabove-described technologies do not require a user to utilize a searchengine in order for the user to discover information about a person thatinterests the user. Fifth, by obtaining names of persons via theknowledge graph, the above-described technologies avoid crawling webpages and/or searching a web index to obtain such information. Thissaves both computing and network resources.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an example computing environmentthat facilitates automatic identification of a topic that is of interestto a person referenced in an article.

FIG. 2 is a symbolic depiction of an example knowledge graph.

FIG. 3 is an example graphical user interface (GUI) of a newsapplication.

FIG. 4 is an example webpage that includes information about a topicthat is of interest to a person.

FIG. 5 is a flow diagram that illustrates an example methodologyexecuted by a computing system that facilitates determining a topic thatis of interest to a person.

FIG. 6 is a flow diagram that illustrates an example methodologyexecuted by a computing system that facilitates displaying informationpertaining to a topic that is of interest to a person.

FIG. 7 is an example computing device.

Various technologies pertaining to automatic identification of a topicthat is of interest to a person referenced in an article are nowdescribed with reference to the drawings, where like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of one or moreaspects. It may be evident, however, that such aspect(s) may bepracticed without these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing one or more aspects. Further, it is to beunderstood that functionality that is described as being carried out bycertain system components may be performed by multiple components.Similarly, for instance, a component may be configured to performfunctionality that is described as being carried out by multiplecomponents.

DETAILED DESCRIPTION

As noted above, an article may be selected by a user for viewing basedupon an interest of the user in a subject of the article. In an example,the subject of the article may be a politician and the user may wish toview additional information about the politician beyond what ispresented in the article, such as which sports teams the politiciansupports, which bands the politician likes, and so forth. Conventionalapproaches to presenting additional information pertaining to a personreferenced in an article rely upon search engine searches initiated by acomputing device operated by the user. As such, conventional approachesreduce user engagement with a news application presenting the article.

To facilitate presentation of information about a person that a user isinterested in, a computing system executes a search over a knowledgegraph based upon an identifier for a profession (e.g., politician). Thesearch produces search results, where the search results include anentry in the knowledge graph for a person that belongs to the profession(e.g., the person is a politician). The computing system determines aname of the person based upon the entry in the knowledge graph. Thecomputing system retrieves a plurality of articles (e.g., news articles)based upon the name of the person. The computing system determines, viaa named entity recognition model, a topic (e.g., a television show) thatis of interest to the person based upon content of at least one of theplurality of articles. The computing system retrieves a uniform resourcelocator (URL) of a webpage that includes information about the topic andstores the URL, a name of the person, and a name of the topic incomputer-readable storage. In an example where the topic is a televisionshow, the webpage is an entry in an online encyclopedia for thetelevision show or an official webpage of the television show.

The computing system obtains a computer-readable article, where thearticle references a plurality of entities including the person. It iscontemplated that the article may be about a topic that is differentthan a topic of the at least one of the plurality of news articles. Thecomputing system determines, via a coreference resolution model, thatthe person is a dominant entity of the article based upon a number ofreferences to the person within the article. The computing systemdetermines, via a sentiment analysis model, whether the articleexpresses non-negative sentiment based upon content of the article. Whenthe article expresses non-negative sentiment (and when the person is adominant entity of the article), the computing system retrieves the URLfor the webpage that includes information about a topic that is ofinterest to the person based upon the name of the person in the article.Upon receiving a request for the article from a computing device, thecomputing system causes the article and a link to be presentedconcurrently on a display, where the URL is embedded within the link.According to embodiments, the link is in the form of a question. In aspecific example where the person is “Politician X” and the topic ofinterest to “Politician X” is “TV Show Y”, the link displayed on thedisplay is “Did you know that Politician X loves TV show Y?” When thelink is selected, the webpage that includes information about the topicof interest to the person is retrieved and presented on the display.

The above-described technologies present various advantages overconventional technologies for discovering information about interests ofa person. First, by leveraging information within a knowledge graph, theabove-described technologies enable interests of a wide variety ofpeople of varying professions to be discovered. Second, theabove-described technologies ensure that a link with additionalinformation about an interest of a person is inserted into an articlewhen the article is primarily directed towards the person (e.g., theperson is the dominant entity of the article). This can save user timeand effort and can save computing and network resources by avoidingsearch engine procedures. Third, via sentiment analysis, theabove-described technologies ensure that a link with additionalinformation about a person is not inserted into an article in aninappropriate circumstance, such as when the article pertains to atragic event. Fourth, unlike conventional technologies, theabove-described technologies do not require a user to utilize a searchengine in order for the user to discover information about a person thatinterests the user. Fifth, by obtaining names of persons via theknowledge graph, the above-described technologies avoid crawling webpages and/or searching a web index to obtain such information. Thissaves both computing and network resources.

With reference to FIG. 1 , an example computing environment 100 thatfacilitates automatic identification of a topic that is of interest to aperson referenced in an article is illustrated. The computingenvironment 100 includes a computing system 102. The computing system102 includes a processor 104, memory 106, and a data store 108. The datastore 108 stores a computer-implemented knowledge graph 110. Accordingto embodiments, the computing system 102 is a cloud-based computingplatform that is distributed across multiple computing devices.

The knowledge graph 110 includes nodes and edges connecting the nodes,where the nodes represent entities (e.g., people, places, things, ideas,events, organizations, etc.) or attributes of the entities and where theedges are indicative of relationships between the entities themselves orrelationships between the entities and the attributes. In an example, afirst node in the knowledge graph 110 represents a person who is anathlete, a second node in the knowledge graph 110 represents a sportsteam, and a first edge in the knowledge graph 110 that connects thefirst node and the second node is assigned criteria that indicates thatthe person plays for the sports team. In another example, a third nodein the knowledge graph 110 represents a profession (e.g., athlete), andthe first node is connected to the third node in the knowledge graph 110via a second edge that is assigned criteria that indicates that theperson is an athlete. Nodes in the knowledge graph 110 that represententities include names of the entities. Nodes in the knowledge graph 110that represent entities may be assigned unique identifiers so as todisambiguate entities that share the same name. According to someembodiments, nodes and/or edges within the knowledge graph 110 comprisemetadata that enables information about the entities to be retrieved.According to some embodiments, nodes and/or edges within the knowledgegraph 110 store information about the entities. According toembodiments, an entry for an entity in the knowledge graph 110 isrepresented by a node for the entity in the knowledge graph 110 andnodes (representing other entities and/or attributes) that are connectedto the node via (one or more) edges. According to embodiments, nodesand/or edges within the knowledge graph 110 have confidence valuesassigned thereto. In an example, a first node representing a person isconnected to a second node representing a profession via an edge in theknowledge graph 110. The second node and/or the second edge has aconfidence value assigned thereto, where the confidence value isindicative of a certainty that the person belongs to the profession.

Turning now to FIG. 2 , a symbolic depiction of an example knowledgegraph 200 is illustrated. The knowledge graph 200 may be or include theknowledge graph 110 or the knowledge graph 110 may be or include theknowledge graph 200. The knowledge graph 200 includes a first node 202that represents a first person. The first person may be a person ofpublic interest (e.g., a celebrity). According to examples, the firstperson is an actor, a politician, an athlete, a musician, etc. Theknowledge graph 200 includes a second node 204 that represents a firstentity, where the second node 204 is connected to the first node 202 bya first edge 206, where the first edge 206 is assigned criteria that isindicative of a relationship between the first person and the firstentity. In an example where the first person is a politician, the secondnode 204 represents a political party and the first edge 206 is assignedcriteria indicating that the first person belongs to the politicalparty. The knowledge graph 200 includes a third node 208 that representsa second entity, where the third node 208 is connected to the first node202 by a second edge 210, and where the second edge 210 is assignedcriteria that is indicative of a relationship between the first personand the second entity. Following the example given above, the third node208 represents a spouse of the first person and the second edge 210 isassigned criteria indicating that the first person is married to thespouse. The knowledge graph 200 includes a fourth node 212 thatrepresents a first attribute, where the fourth node 212 is connected tothe first node 202 by a third edge 214, and where the third edge 214 isassigned criteria indicating that the first person has the firstattribute. Example attributes include profession, popularity, age,weight, place of residence, etc. Following the example given above, thefirst attribute is “politician” (e.g., the first person is apolitician). It is to be understood that the knowledge graph 200 mayinclude additional nodes and edges (not depicted in FIG. 2 ).

Turning back to FIG. 1 , the memory 106 of the computing system 102includes an insight extractor application 112 (also referred to as “theinsight extractor 112”) loaded therein. As will be described in greaterdetail below, the insight extractor 112 is configured to (1) determineone or more topics of interest of a person, (2) store informationpertaining to the one or more topics of interest of the person incomputer-readable storage, and (3) cause the information pertaining tothe one or more topics of interest of the person to be presented tousers.

The insight extractor 112 includes a graph search component 114. Thegraph search component 114 is configured to search the knowledge graph110 based upon search criteria. In an example, the search criteria is atype of profession, such as actor, athlete, musician, politician, socialmedia influencer, etc. As such, the graph search component 114 generatessearch results upon searching the knowledge graph 110, where the searchresults include names of persons referenced in the knowledge graph 110who belong to the profession. In another example, the search criteria isan attribute. In an example, the graph search component 114 identifiespersons that are social media influencers.

The insight extractor 112 further includes a named entity recognitionmodel 116. The named entity recognition model 116 is configured toidentify entities referenced in a plurality of articles 118 (e.g. newsarticles) and relationships between the entities based upon content(e.g., text) of the plurality of articles 118. The named entityrecognition model 116 determines topics that are of interest to personsbased upon the relationships. The named entity recognition model 116receives the plurality of articles 118 from a plurality of electronicsources 120 over a network 122 (e.g., the Internet, intranet, etc.) Inan example, the named entity recognition model 116 identifies a personreferenced in an article (from the plurality of articles 118), an entityreferenced in the article, and an interest relationship between theperson and the entity, that is, the person is interested in the entity.The entity that is of interest to the person may be another person, anorganization, a place, a type of media, an idea, an event, or a thing.The entity that is of interest to the person is a topic of interest ofthe person. Topics of interest to the person may include one or more ofan athlete, a sports team, a sports league, a sports event, anentertainment event, a performer, an actor, a television show, adirector, a video game, a politician, or a political party.

According to embodiments, the named entity recognition model 116 is orincludes a deep learning model. The deep learning model includes aninput layer, one or more hidden layers, and an output layer, where thelayers comprise nodes, where the nodes within a layer are connected tonodes within a different layer by edges that have learned weightsassigned thereto, and where the weights are influenced by content of asecond plurality of articles (not depicted in FIG. 1 ) that are used totrain the deep learning model. According to embodiments, the secondplurality of articles include references to different people expressinginterest in different entities (e.g., topics), and such articles areused as positive training examples in training the deep learning model.

The insight extractor 112 further includes a coreference resolutionmodel 124. The coreference resolution model 124 is configured todetermine a dominant entity (from amongst a plurality of entities)referenced in an article 126 based upon a number of references to theentity (e.g., a person, such as a celebrity) in the article 126. Thereferences to the entity may include one or more of a full name of theentity, a first name of the entity (when the entity is a person), a lastname of the entity (when the entity is a person), a subject pronounreferring to the entity, an object pronoun referring to the entity, apossessive adjective referring to the entity, a possessive pronounreferring to the entity, or a reflexive pronoun referring to the person.In an example where the article 126 includes the sentence “Jane said shewould go to the store.”, the coreference resolution model 124 identifiestwo references to Jane: “Jane” and “she”. According to embodiments, thedominant entity referenced in the article 126 is an entity with agreatest number of references within the article 126. According to someembodiments, the dominant entity referenced in the article 126 is anentity with a greatest number of references within the article 126 thathas a particular type (e.g., a person that is referred to a greatestnumber of times within the article 126 in comparison to other personsreferred to in the article 126). According to embodiments, thecoreference resolution model 124 may be or include a rule-based model, amention-pair model, a mention-ranking model, or a clustering-basedmodel.

The insight extractor 112 further includes a sentiment analysis model128. The sentiment analysis model 128 is configured to determine whetherthe article 126 expresses non-negative sentiment (e.g., positivesentiment or neutral sentiment) based upon content of the article 126.According to some embodiments, the sentiment analysis model 128classifies the article 126 as positive or negative based upon content ofthe article 126. In an example, the sentiment analysis model 128classifies the sentence “Actor John Doe's new movie is a huge hit” aspositive and the sentence “Actor John Doe was arrested yesterday” asnegative. According to other embodiments, the sentiment analysis model128 classifies the article 126 as positive, neutral, or negative basedupon content of the article 126. According to some embodiments, thesentiment analysis model 128 is a deep learning model that outputs,based upon content of an input article, a value that ranges from 0.0 to1.0, where a value of 0.0 indicates that the input article expressesnegative sentiment and where a value of 1.0 indicates that the inputarticle expresses positive sentiment. The deep learning model includesan input layer, a plurality of hidden layers, and an output layer, wherethe layers comprise nodes, where the nodes within a layer are connectedto nodes within a different layer by edges that have learned weightsassigned thereto, where the learned weights are influenced by content ofa third plurality of articles (not shown in FIG. 1 ).

The sentiment analysis model 128 may utilize knowledge-based techniques,statistical methods, or a combination thereof in order to determinewhether the article 126 expresses a non-negative sentiment. Theknowledge-based techniques classify the article 126 as positive ornegative based upon presence of words such as “happy”, “sad”, etc.Statistical methods used by the sentiment analysis model 128 may includelatent semantic analysis, support vector machines, bag of words, anddeep learning models.

The insight extractor 112 may further include a delivery component 130.The delivery component 130 is configured to transmit the article 126 (aswell as other articles) to different computing devices operated bydifferent users, where the article 126 may include a link to a webpagethat includes information about a topic that is of interest to a personreferenced in the article 126.

The data store 108 may further store entity interest data 132 for aplurality of persons that are referenced in the knowledge graph 110,where the entity interest data 132 for a person includes a name of theperson, a name of a topic of interest to the person, and a URL of awebpage that includes information pertaining to the topic that is ofinterest to the person. In an example, the webpage may be an entry in anonline encyclopedia for the topic. In another example, the webpage maybe an official webpage of the topic. In yet another example, the webpagemay be a webpage for the topic that is provided by a news aggregator. Ina further example, the webpage may include URLs for webpages, where eachwebpage in the webpage references both the person and the topic that isof interest to the person. In yet another example, the webpage includesinformation about the topic and URLs for different webpages, where someof the URLs are for webpages that discuss both the topic and the personin relation to one another and where some of the URLs are for webpagesthat include additional information about the topic (beyond theinformation included in the webpage).

The computing environment 100 further includes a computing device 134that is operated by a user 136. The computing device 134 is incommunication with the computing system 102 by way of the network 122.In an example, the computing device 134 is a desktop computing device, alaptop computing device, a tablet computing device, a smartphone, agaming console, or a wearable computing device.

The computing device 134 includes a processor 138 and memory 140, wherethe memory 140 has a news application 142 loaded therein. The newsapplication 142 is configured to present news articles to the user 136(e.g., the article 126). The computing device 134 further includes inputcomponents 144 that enable the user 136 to set forth input to thecomputing device 134. The input components 144 may include a mouse, akeyboard, a trackpad, a scroll wheel, a touchscreen, a camera, a videocamera, a microphone, etc. The computing device 134 also includes outputcomponents 146 that enable the computing device 134 to outputinformation to the user 136. The output components 146 include a display148, where graphical features may be presented thereon. The newsapplication 142 presents a news application graphical user interface(GUI) 150 (also referred to herein as “the GUI 150”) on the display 148.The output components 146 may also include a speaker and/or a hapticfeedback device (not shown in FIG. 1 ).

Although the graph search component 114, the named entity recognitionmodel 116, the coreference resolution model 124, the sentiment analysismodel 128, and the delivery component 130 are described above as beingpart of the insight extractor 112, it is to be understood that some orall of these components/models may be separate applications and thatsome or all of these components/models may execute on separate computingdevices.

Operation of the computing environment 100 is now set forth. It iscontemplated that the article 126 includes a reference to a person 152of public interest (e.g., a celebrity, such as an actor or apolitician). It is further contemplated that at least one article in theplurality of articles 118 also includes a reference to the person 152.It is also contemplated that the knowledge graph 110 includes an entryfor the person 152.

The graph search component 114 of the insight extractor 112 executes asearch over the knowledge graph 110 based upon an identifier for aprofession (e.g., actor, politician, etc.). The search produces searchresults that include entries for persons in the knowledge graph 110 thatbelong to the profession. The graph search component 114 identifies anentry for the person 152 in the search results. The graph searchcomponent 114 obtains a name of the person 152 from the entry for theperson 152 in the knowledge graph 110. In an example, the name of theperson 152 is “John Doe” and the profession of “John Doe” is actor.

The insight extractor 112 selects the person 152 from amongst thepersons. According to embodiments, the insight extractor 112 selects theperson 152 when a confidence value assigned to a node in the knowledgegraph 110 representing a profession of the person 152 exceeds athreshold value. The insight extractor 112 obtains the plurality ofarticles 118 from the plurality of electronic sources 120, where atleast one of the plurality of articles 118 (1) references the person 152and (2) references a topic that is of interest to the person 152. In anexample, the insight extractor 112 transmits a search query to a searchengine (not shown in FIG. 1 ), where the search query includes the nameof the person 152. The search engine returns search results to theinsight extractor 112, where the search results include URLs of theplurality of articles 118. The insight extractor 112 may then access oneor more of the plurality of articles 118 using the URLs. According toembodiments, the insight extractor 112 disregards articles that havedates of publication that are beyond a threshold period of time. In anexample, the insight extractor 112 accesses articles that are datedwithin five years of a current date.

The insight extractor 112 provides content of at least one of theplurality of articles 118 to the named entity recognition model 116. Thenamed entity recognition model 116 identifies a topic that is ofinterest to the person 152 based upon the content of the at least one ofthe plurality of articles 118. In an example, the insight extractor 112provides text of the at least one article to the named entityrecognition model 116. The named entity recognition model 116: (1)identifies the person 152 referenced in the at least one article, (2)identifies other entities referenced in the article, and (3) identifiesrelationships between the person 152 and the other entities. It iscontemplated that the relationships include “interest” relationships,that is, that certain entities referenced in the at least one articleare of interest to the person 152. The entities that are of interest tothe person 152 are topics of interest of the person 152. As such, thenamed entity recognition model 116 identifies the topic that is ofinterest to the person 152 based upon the content of the at least onearticle, where the content of the at least one news article includes aname of the person 152 and a name of the topic. According to examples,the topic that is of interest to the person 152 is another person, suchas an athlete, an actor, a politician, a musician, a director, a socialmedia influencer, an author, etc., an organization, such as a politicalparty, a sports team, a band, etc., a place, such as a city or acountry, a type of media, such as a particular movie or television show,an idea, such as a particular hobby of the person 152, or a thing.

In an example where the person 152 is named “John Doe”, the at least onearticle includes the sentence “Actor John Doe often attends BasketballTeam X's games.” Following the example, the named entity recognitionmodel 116 identifies an interest relationship between “John Doe” and“Basketball Team X,” that is, “Basketball Team X” is a topic of interestto “John Doe.” The insight extractor 112 may identify topics of interestfor different persons belonging to the profession (as well as topics ofinterest for persons belonging to other professions) using theabove-described processes. It is contemplated that the insight extractor112 periodically (e.g., once a day, once a week, once a year, etc.)repeats the steps of searching the knowledge graph 110 and identifyingtopics of interest to the person 152.

Subsequent to identifying the topic that is of interest to the person152, the insight extractor 112 retrieves a URL of a webpage thatincludes information pertaining to the topic that is of interest to theperson 152. In one example, the URL is an entry for the topic in anonline encyclopedia. In another example, the URL is an official webpageof the topic. In yet another example, the URL is a current news articlepertaining to the topic. Following the specific example given abovewhere the person 152 is “John Doe” and where the topic of interest to“John Doe” is “Basketball Team X”, the URL is an entry for “BasketballTeam X” in an online encyclopedia. The insight extractor 112 stores thefollowing information as part of the entity interest data 132 in thedata store 108: a name of the person 152, a name of the topic that is ofinterest to the person 152, and one or more URLs of webpages thatinclude information pertaining to the topic that is of interest to theperson 152. The insight extractor 112 may also store, as part of theentity interest data 132, a date in which the topic that is of interestto the person 152 was determined. According to embodiments, the insightextractor 112 may modify the knowledge graph 110 to reflect the interestof the person 152 in the topic. In an example, the insight extractor 112may cause an edge to be added to the knowledge graph 110, where the edgeconnects a node representing the person 152 within the knowledge graph110 and a node representing the topic within the knowledge graph 110,where the node is assigned criteria indicating that the entity is ofinterest to the person 152.

According to embodiments, the insight extractor 112 updates topics ofinterest to the person 152 over time. In an example, via theabove-described processes, the insight extractor 112 determines that theperson 152 is a fan of “Basketball Team X” using articles that are datedwithin a first period of time and stores the interest of the person 152in “Basketball Team X” as part of the entity interest data 132. Sometimethereafter, the person 152 switches their support to “Basketball TeamY.” The insight extractor 112, via the above-described processes,determines that the person 152 is a fan of “Basketball Team Y” usingarticles that are dated within a second period of time that occurs afterthe first period of time. The insight extractor 112 updates the entityinterest data 132 for the person 152 to replace the interest of theperson in “Basketball Team X” with “Basketball Team Y.”

The insight extractor 112 receives the article 126 from an electronicsource in the plurality of electronic sources 120. The article 126 maybe a recently published news article. The article 126 may be about afirst topic. The article 126 references a plurality of entities (e.g.,persons, places, things, ideas, events, etc.), where the plurality ofentities include the person 152. It is contemplated that the pluralityof entities referenced in the article 126 do not include an entitycorresponding to a second topic, where the second topic is of interestto the person 152 (determined via the named entity recognition model116). The insight extractor 112 provides content of the article 126 tothe coreference resolution model 124. The coreference resolution model124 determines that the person 152 is a dominant entity of the article126 based upon a number of references to the person 152 within thearticle 126. The person 152 may be a dominant entity of the article 126when the person is a primary subject of the article 126. In this manner,the coreference resolution model 124 ensures that the reference to theperson 152 in the article 126 is not a tangential reference and that thearticle 126 is primarily directed towards the person 152. Following theexample given above where the person 152 is an actor named “John Doe”,the coreference resolution model 124 determines that “John Doe” is adominant entity of the article 126 when the article 126 is an interviewwith “John Doe.”

The insight extractor 112 provides content of the article 126 to thesentiment analysis model 128 and the sentiment analysis model 128outputs an indication as to whether the article 126 expresses anon-negative sentiment based upon the content of the article 126. Whenthe article 126 expresses a non-negative sentiment (and when the person152 is the dominant entity of the article 126), the insight extractor112 executes a search over the entity interest data 132 based upon thename of the person 152 referenced in the article 126. The searchproduces search results, where the search results include a URL of awebpage that includes information pertaining to the (second) topic thatis of interest to the person 152. Following the example given abovewhere the person 152 is an actor named “John Doe” and where the (second)topic of interest to “John Doe” is “Basketball Team X”, the searchresults include a URL of a webpage that includes information about“Basketball Team X.”

Upon receiving a request for the article 126 from the news application142 (or upon receiving a request from another application executing onthe computing device 134) and when the article 126 is determined toexpress non-negative sentiment and when the person 152 is the dominantentity of the article 126, the delivery component 130 of the insightextractor 112 transmits the article 126 and a link to the newsapplication 142 (or another application executing on the computingdevice 134), where the URL of the webpage that includes informationpertaining to the (second) topic of interest to the person 152 isembedded within the link. According to some embodiments, the deliverycomponent 130 inserts the link into the article 126. According to otherembodiments, the delivery component 130 transmits the article 126 andthe link to the news application 142, where the article 126 is displayedin a region of the GUI 150 reserved for news articles and where the linkis displayed in a region of the GUI 150 that is reserved for non-newsarticle data. According to embodiments, the link is in the form of aquestion that includes the name of the person 152 and the name of the(second) topic that is of interest to the person 152. Following theexample given above where the person 152 is an actor named “John Doe”and where the entity of interest is “Basketball Team X”, the link is inthe form of “Did you know that John Doe is a huge fan of Basketball TeamX?” The news application 142 presents the article 126 and the link onthe display 148. When the link is selected by the user 136, thecomputing device 134 retrieves the webpage corresponding to the URLembedded in the link and the computing device 134 presents the webpageon the display 148. The user 136 may then view the information about the(second) topic that is of interest to the person 152. When the article126 is determined to express negative sentiment (or when the person 152is not the dominant entity of the article 126), the delivery component130 transmits the article 126 (without the link) to the news application142, where the article 126 is presented on the display 148. In thismanner, the insight extractor 112 ensures that links to webpagesincluding information about a topic of interest of the person 152 arenot presented in inappropriate circumstances.

According to embodiments, the insight extractor 112 determines that theperson 152 is included in a plurality of persons based upon a name ofthe person in the article 126. The article 126 (about the first topic)and the link to the webpage that includes information pertaining to the(second) topic are displayed in accordance with the person 152 beingincluded in the plurality of persons.

According to embodiments, the insight extractor 112 obtains a name ofthe person 152 from the article 126. The insight extractor 112, via thegraph search component 114, executes a search over the knowledge graph110 based upon the name of the person 152. The insight extractor 112identifies a profession of the person 152 from an entry for the person152 in the knowledge graph 110. The insight extractor 112 determinesthat the profession of the person 152 is included in a plurality ofprofessions (e.g., actor, politician, etc.). The article 126 (about thefirst topic) and the link to the webpage that includes informationpertaining to the (second) topic are displayed in accordance with theperson 152 being included in the plurality of professions.

It is to be understood that the insight extractor 112 may identifydifferent topics of interest of the person 152 and that the insightextractor 112 may store information pertaining to the different topicsin the entity interest data 132. In an example, the insight extractor112 identifies a first topic and a second topic that are of interest tothe person 152 using the above-described processes. Following theexample, the insight extractor 112 obtains a first URL of a firstwebpage that includes information about the first topic and a second URLof a second webpage that includes information about the second topic.According to some embodiments, the insight extractor 112 assignsrankings to the first topic and the second topic based upon rankingcriteria. The ranking criteria may include user data of the user 136,popularity of the first topic and the second topic as determined by anumber of times each of the first webpage and the second webpage wereaccessed, a number of articles that exist on the web about the firsttopic and a number of articles that exist on the web about the secondtopic, a number of articles that exist on the web that reference theperson 152 with respect to the first topic and a number of articles thatexist on the web that reference the person 152 with respect to thesecond topic, manually set forth relevance scores, and so forth.According to embodiments, the insight extractor 112 selects one of thefirst topic or the second topic based upon the rankings assigned to thefirst topic and the second topic and causes a link with one of the firstURL or the second URL embedded therein to be presented on the display148. According to other embodiments, the insight extractor 112 may causeboth a first link (which has the first URL embedded therein) and thesecond link (which has the second URL embedded therein) to be presentedon the display 148.

Turning now to FIG. 3 , an example GUI 300 of the news application 142is illustrated. The GUI 300 is presented on the display 148 of thecomputing device 134. The GUI 300 may be or include the GUI 150 or theGUI 150 may be or include the GUI 300. The GUI 300 includes a firstregion 302 that displays the article 126. It is contemplated that thearticle may be about a first topic. The GUI 300 also includes a secondregion 304 that displays the link. In the example shown in FIG. 3 , thelink is in the form of a question that references the person 152 (“JohnDoe”) and a second topic (“Basketball Team X”) that is of interest tothe person 152, where the second topic is different than the firsttopic. In the example depicted in FIG. 3 , the (first) topic of the newsarticle 126 is “John Doe” starring in a new movie, whereas the (second)topic of interest to “John” Doe” is “Basketball Team X.” The URL of thewebpage that includes information about the (second) topic that is ofinterest to the person 152 is embedded within the link. When the link isselected, the computing device 134 accesses the webpage using the URLand presents the webpage on the display 148.

Although not depicted in FIG. 3 , it is to be understood that more thanone link may be presented within the GUI 300. In an example, the GUI 300includes a first link to a first topic of interest of the person 152 anda second link to a second topic of interest of the person 152. When thefirst link is selected, a webpage about the first topic is presented onthe display 148. When the second link is selected, a webpage about thesecond topic is presented on the display 148. In a specific example, thefirst topic of interest to the person 152 is a music festival and thesecond topic of interest to the person 152 is an entertainer.

Referring now to FIG. 4 , an example webpage 400 that includesinformation pertaining to a topic of interest to the person 152 isdepicted. As “John Doe” is interested in “Basketball Team X,” thewebpage 400 includes information about “Basketball Team X.” According tosome embodiments, the webpage 400 includes one or more links to articlesabout the topic of interest to the person 152. According to someembodiments, the webpage 400 includes one or more links to articles thatreference both the person 152 (“John Doe”) and the topic of interest(“Basketball Team X”). According to some embodiments, articles that arelinked to in the webpage 400 are selected based on how recent each ofthe articles were published.

The webpage 400 additionally includes a first link 402 to an officialwebpage of “Basketball Team X”. When the first link 402 is selected, theofficial webpage of “Basketball Team X” is presented on the display 148.The webpage 400 further includes a second link 404 to a webpage of“Basketball Team X” on a sports website. When the second link 404 isselected, the webpage of “Basketball Team X” on the sports website ispresented on the display 148. The webpage 400 also includes a third link406 to a webpage discussing “John Doe” being spotted sitting courtsideat a “Basketball Team X” game with a model named “Jane Smith.” When thethird link 406 is selected, the webpage discussing “John Doe” beingspotted courtside at a “Basketball Team X” game with model “Jane Smith”is presented on the display 148. The webpage 400 additionally includes afourth link 408 to a webpage discussing “John Doe” having a BasketballTeam X tattoo. When the fourth link 408 is selected, the webpagediscussing “John Doe” having a “Basketball Team X” tattoo is presentedon the display 148.

FIGS. 5 and 6 illustrate example methodologies relating to identifyingtopics that are of interest to persons referenced in articles. While themethodologies are shown and described as being a series of acts that areperformed in a sequence, it is to be understood and appreciated that themethodologies are not limited by the order of the sequence. For example,some acts can occur in a different order than what is described herein.In addition, an act can occur concurrently with another act. Further, insome instances, not all acts may be required to implement a methodologydescribed herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

Referring now to FIG. 5 , an example methodology 500 executed by acomputing system that facilitates determining a topic that is ofinterest to a person is illustrated. The methodology 500 begins at 502,and at 504, the computing system executes a search over a knowledgegraph based upon an identifier for a profession. At 506, the computingsystem identifies a person referenced in the knowledge graph based uponsearch results for the search, where the person belongs to theprofession. At 508, the computing system identifies a plurality ofwebpages that reference the person. At 510, the computing systemidentifies a topic that is of interest to the person based upon contentof the plurality of webpages. At 512, the computing system stores datapertaining to the topic and the person in computer-readable storage. Thedata includes a name of the person, a name of the topic that is ofinterest to the person, and a URL of a webpage that includes informationabout the topic. The methodology 500 concludes at 514.

Turning now to FIG. 6 , an example methodology 600 executed by acomputing system that facilitates displaying data pertaining to a topicthat is of interest to a person is illustrated. The methodology 600begins at 602, and at 604, the computing system obtains a first article,where the first article is about a first topic. The first articlereferences a plurality of entities. The plurality of entities include aperson. At 606, the computing system identifies the person referenced inthe first article as a dominant entity of the first article based upon anumber of references to the person within the first article. At 608, thecomputing system determines whether the first article expresses negativesentiment based upon content of the first article. At 610, in accordancewith a determination that the first article does not express negativesentiment, the computing system retrieves a URL of a webpage thatincludes information pertaining to a second topic, where the secondtopic is of interest to the person. The second topic that is of interestto the person is determined based upon an entry for the person in acomputer-implemented knowledge graph and a second article thatreferences both the person and the second topic. At 612, upon receivinga request for the first article from a computing device, the computingsystem causes the first article and a link to be displayed on a display.The URL of the webpage is embedded in the link. The webpage is presentedon the display upon the link being selected. At 614, in accordance witha determination that the first article expresses negative sentiment andupon receiving a request for the first article, the computing systemcauses the first article to be presented on a display (withoutpresenting the link). The methodology 600 concludes at 616.

Referring now to FIG. 7 , a high-level illustration of an examplecomputing device 700 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. In an example, thecomputing device 700 may be used in a system that determines a topicthat is of interest to a person referenced in a knowledge graph. By wayof another example, the computing device 700 can be used in a systemthat displays data pertaining to a topic that is of interest to a personreferenced in a knowledge graph. The computing device 700 includes atleast one processor 702 that executes instructions that are stored in amemory 704. The instructions may be, for instance, instructions forimplementing functionality described as being carried out by one or morecomponents discussed above or instructions for implementing one or moreof the methods described above. The processor 702 may access the memory704 by way of a system bus 706. In addition to storing executableinstructions, the memory 704 may also store knowledge graphs, entityinterest data, news articles, computer-implemented models (named entityrecognition models, coreference resolution models, sentiment analysismodel), etc.

The computing device 700 additionally includes a data store 708 that isaccessible by the processor 702 by way of the system bus 706. The datastore 708 may include executable instructions, knowledge graphs, entityinterest data, news articles, computer-implemented models (named entityrecognition models, coreference resolution models, sentiment analysismodel), etc. The computing device 700 also includes an input interface710 that allows external devices to communicate with the computingdevice 700. For instance, the input interface 710 may be used to receiveinstructions from an external computer device, from a user, etc. Thecomputing device 700 also includes an output interface 712 thatinterfaces the computing device 700 with one or more external devices.For example, the computing device 700 may display text, images, etc. byway of the output interface 712.

It is contemplated that the external devices that communicate with thecomputing device 700 via the input interface 710 and the outputinterface 712 can be included in an environment that providessubstantially any type of user interface with which a user can interact.Examples of user interface types include graphical user interfaces,natural user interfaces, and so forth. For instance, a graphical userinterface may accept input from a user employing input device(s) such asa keyboard, mouse, remote control, or the like and provide output on anoutput device such as a display. Further, a natural user interface mayenable a user to interact with the computing device 700 in a manner freefrom constraints imposed by input devices such as keyboards, mice,remote controls, and the like. Rather, a natural user interface can relyon speech recognition, touch and stylus recognition, gesture recognitionboth on screen and adjacent to the screen, air gestures, head and eyetracking, voice and speech, vision, touch, gestures, machineintelligence, and so forth.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 700 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 700.

The present disclosure relates to identifying a topic that is ofinterest to a person referenced in an article according to at least thefollowing examples provided in the section below:

(A1) In one aspect, some embodiments include a method (e.g., 600)executed by a processor (e.g., 104) of a computing system (e.g., 102).The method includes obtaining (e.g., 604) a computer-readable firstarticle (e.g., 126) about a first topic, where the first articlereferences a plurality of entities, and further where the plurality ofentities include a person (e.g., 152). The method further includesidentifying (e.g., 606) the person as a dominant entity of the firstarticle based upon a number of references to the person within the firstarticle. The method additionally includes determining (e.g., 608)whether the first article expresses negative sentiment based uponcontent of the first article. The method also includes in accordancewith a determination that the first article does not express negativesentiment: (a) retrieving (e.g., 610) a uniform resource locator (URL)of a webpage about a second topic that is of interest to the person,wherein the second topic is determined to be of interest to the personbased upon an entry for the person in a computer-implemented knowledgegraph (e.g., 110, 200) and a second article that references both theperson and the second topic and (b) upon receiving a request for thefirst article from a computing device (e.g., 134), causing (e.g., 612)the first article and a link to be concurrently displayed on a display(e.g., 148), where the URL of the webpage is embedded in the link. Themethod further includes in accordance with a determination that thefirst article expresses negative sentiment: upon receiving the requestfor the first article from the computing device, causing (e.g., 614) thefirst article to be displayed on the display without concurrentlydisplaying the link.

(A2) In some embodiments of the method of A1, the method furtherincludes prior to obtaining the first article, executing a search overthe knowledge graph based upon an identifier for a profession. Themethod additionally includes responsive to executing the search,identifying a plurality of persons referenced in the knowledge graphbased upon search results for the search, where each person in theplurality of persons belongs to the profession, and further where theplurality of persons includes the person. The method also includesobtaining names of each person in the plurality of persons from entriesfor each person in the knowledge graph.

(A3) In some embodiments of the method of A2, the method furtherincludes subsequent to obtaining the names of each person in theplurality of persons, identifying a plurality of articles that referencethe person based upon a name of the person, where the plurality ofarticles include the second article that references both the person andthe second topic. The method also includes providing content of thesecond article as input to a computer-implemented named-entityrecognition model (e.g., 116), where the named-entity recognition modeloutputs an indication that the second topic referenced in the secondarticle is of interest to the person based upon the content of thesecond article. The method further includes storing entity interest data(e.g., 132) in computer-readable storage, wherein the entity interestdata indicates that the second topic is of interest to the person.

(A4) In some embodiments of the method of A3, the named-entityrecognition model is a deep learning model.

(A5) In some embodiments of any of the methods of A1-A4, a third topicis of interest to the person. The method further includes ranking thesecond topic and the third topic based upon ranking criteria. The methodadditionally includes selecting the second topic based upon a rank ofthe second topic and a rank of the third topic, where the link isdisplayed on the display concurrently with the first article based uponthe second topic being selected.

(A6) In some embodiments of any of the methods of A1-A5, the article ispresented within a first region (e.g., 302) of a graphical userinterface (GUI) (e.g., 300, 150) and the link is presented within asecond region (e.g., 304) of the GUI.

(B1) In another aspect, some embodiments include a computing system(e.g., 102) that includes a processor (e.g., 104) and memory (e.g.,106). The memory stores instructions that, when executed by theprocessor, cause the processor to perform any of the methods describedherein (e.g., any of A1-A6).

(C1) In yet another aspect, some embodiments include a non-transitorycomputer-readable storage medium that includes instructions that, whenexecuted by a processor (e.g., 104) of a computing system (e.g., 102),cause the processor to perform any of the methods described herein(e.g., any of A1-A6).

(D1) In another aspect, some embodiments include a method executed by acomputing system (e.g., 102) that includes a processor (e.g., 104) andmemory (e.g., 106). The method includes obtaining a computer-readablefirst article (e.g., 126) about a first topic, where the first articlereferences a plurality of entities, and further where the plurality ofentities include a first person (e.g., 152). The method further includesidentifying the first person as a dominant entity of the first articlebased upon a number of references to the first person within the firstarticle. The method additionally includes determining whether the firstarticle expresses negative sentiment based upon content of the firstarticle. The method also includes in accordance with a determinationthat the first article does not express negative sentiment, retrieving auniform resource locator (URL) of a webpage about a second topic that isof interest to the first person, where the second topic is determined tobe of interest to the first person based upon an entry for the firstperson in a computer-implemented knowledge graph (e.g., 110, 200) and asecond article that references both the first person and the secondtopic. The method further includes upon receiving a request for thefirst article from a computing device (e.g., 134), causing the firstarticle and a link to be concurrently displayed on a display (e.g.,148), where the URL of the webpage is embedded in the link.

(D2) In some embodiments of the method of D1, the method furtherincludes prior to obtaining the first article, executing a search overthe knowledge graph based upon an identifier for a profession. Themethod additionally includes responsive to executing the search,identifying a plurality of persons referenced in the knowledge graph,where each person in the plurality of persons belongs to the profession,and further where the plurality of persons includes the first person.The method also includes for each person in the plurality of persons,obtaining a name of the person from an entry for the person in theknowledge graph.

(D3) In some embodiments of the method of D2, the profession is one ormore of an actor, a politician, a social media influencer, or anathlete.

(D4) In some embodiments of any of the methods of D2-D3, the methodfurther includes subsequent to obtaining a name of the first person,identifying a plurality of articles that reference the first personbased upon the name of the first person, where the plurality of articlesinclude the second article. The method additionally includes providingcontent of the second article as input to a computer-implementednamed-entity recognition model (e.g., 116), where the named-entityrecognition model outputs an indication that the second topic referencedin the second article is of interest to the first person based upon thecontent of the second article.

(D5) In some embodiments of any of the methods of D2-D4, the methodfurther includes subsequent to obtaining the first article, determiningthat the first person is included in the plurality of persons based upona name of the first person, where the first article and the link arecaused to be concurrently displayed in accordance with the first personbeing included in the plurality of persons.

(D6) In some embodiments of the method of D4, the method furtherincludes subsequent to identifying that the second topic is of interestto the first person, obtaining the URL of the webpage that includes theinformation pertaining to the second topic. The method also includesstoring the URL of the webpage in a data store (e.g., 108).

(D7) In some embodiments of any of the methods of D1-D6, the methodfurther includes obtaining a name of the first person from the firstarticle. The method additionally includes executing a search over theknowledge graph based upon the name of the first person. The method alsoincludes identifying a profession of the first person from the knowledgegraph. The method additionally includes determining that the professionof the first person is included in a plurality of professions, where thefirst article and the link are caused to be concurrently displayed inaccordance with the profession of the first person being included in theplurality of professions.

(D8) In some embodiments of any of the methods of D1-D7, causing thefirst article and the link to be concurrently displayed on the displayincludes inserting the link into the first article and transmitting thefirst article with the link inserted therein to the computing device,where the first article and the link are presented concurrently on thedisplay by the computing device.

(D9) In some embodiments of any of the methods of D1-D8, the secondtopic relates to one or more of an athlete, a sports team, a sportsleague, a sports event, an entertainment event, a performer, an actor, atelevision show, a director, or a politician.

(D10) In some embodiments of any of the methods of D1-D9, the pluralityof entities referenced in the first article further include a firstentity and the first person is determined to be the dominant entity ofthe first article in accordance with the number of references to thefirst person within the first article exceeding a number of referencesto the first entity within the first article.

(D11) In some embodiments of any of the methods of D1-D10, thereferences to the first person within the first article include one ormore of a full name of the first person, a first name of the firstperson, a last name of the first person, a subject pronoun referring tothe first person, an object pronoun referring to the first person, apossessive adjective referring to the first person, a possessive pronounreferring to the first person, or a reflexive pronoun referring to thefirst person.

(E1) In another aspect, some embodiments include a computing system(e.g., 102) including a processor (e.g., 104) and memory (e.g., 106).The memory stores instructions that, when executed by the processor,cause the processor to perform any of the methods described herein(e.g., any of D1-D11).

(F1) In yet another aspect, some embodiments include a non-transitorycomputer-readable storage medium that includes instructions that, whenexecuted by a processor (e.g., 104) of a computing system (e.g., 102),cause the processor to perform any of the methods described herein(e.g., any of D1-D11).

(G1) In another aspect, some embodiments include a method performed by acomputing system (e.g., 102) that includes a processor (e.g., 104). Themethod includes obtaining a computer-readable article (e.g., 126) abouta first topic, wherein the first article references a plurality ofentities, and further wherein the plurality of entities include a firstperson (e.g., 152). The method further includes identifying the firstperson referenced in the first article as a dominant entity of the firstarticle based upon a number of references to the first person within thefirst article. The method additionally includes determining whether thefirst article expresses non-negative sentiment based upon content of thefirst article. The method also includes when the first article isdetermined to express non-negative sentiment, retrieving a uniformresource locator (URL) of a webpage about a second topic that is ofinterest to the first person, wherein the second topic is determined tobe of interest to the first person based upon an entry for the firstperson in a computer-implemented knowledge graph (e.g., 110, 200) and asecond article that references both the first person and the secondtopic. The method further includes upon receiving a request for thearticle from a computing device (e.g., 134), transmitting the articleand a link to the computing device for concurrent presentment on adisplay (e.g., 148), where the URL of the webpage is embedded in thelink.

(G2) In some embodiments of the method of G1, the link is displayed onthe display as a question, wherein the question includes a name of thefirst person and a name of the second topic that is of interest to thefirst person.

(G3) In some embodiments of any of the methods G1-G2, the webpageincludes URLs for webpages and each webpage in the webpages referencesboth the first person and the second topic.

(H1) In another aspect, some embodiments include a computing system(e.g., 102) including a processor (e.g., 104) and memory (e.g., 106).The memory stores instructions that, when executed by the processor,cause the processor to perform any of the methods described herein(e.g., any of G1-G3).

(I1) In yet another aspect, some embodiments include a non-transitorycomputer-readable storage medium that includes instructions that, whenexecuted by a processor (e.g., 104) of a computing system (e.g., 102),cause the processor to perform any of the methods described herein(e.g., any of G1-G3).

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. Such computer-readable storage media can includerandom-access memory (RAM), read-only memory (ROM), electricallyerasable programmable read-only memory (EEPROM), compact disc read-onlymemory (CD-ROM) or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tostore desired program code in the form of instructions or datastructures and that can be accessed by a computer. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and blu-ray disc (BD), wheredisks usually reproduce data magnetically and discs usually reproducedata optically with lasers. Further, a propagated signal is not includedwithin the scope of computer-readable storage media. Computer-readablemedia also includes communication media including any medium thatfacilitates transfer of a computer program from one place to another. Aconnection can be a communication medium. For example, if the softwareis transmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

As used herein, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or.” That is, unless specified otherwise, orclear from the context, the phrase “X employs A or B” is intended tomean any of the natural inclusive permutations. That is, the phrase “Xemploys A or B” is satisfied by any of the following instances: Xemploys A; X employs B; or X employs both A and B. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from the context to be directed to asingular form.

Further, as used herein, the terms “component” and “system” are intendedto encompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean serving as an illustration orexample of something, and is not intended to indicate a preference.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the detailed description or theclaims, such term is intended to be inclusive in a manner similar to theterm “comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A computing system, comprising: a processor; andmemory storing instructions that, when executed by the processor, causethe processor to perform acts comprising: obtaining a computer-readablefirst article about a first topic, wherein the first article referencesa plurality of entities, and further wherein the plurality of entitiesinclude a first person; identifying the first person as a dominantentity of the first article based upon a number of references to thefirst person within the first article; determining whether the firstarticle expresses negative sentiment based upon content of the firstarticle; in accordance with a determination that the first article doesnot express negative sentiment, retrieving a uniform resource locator(URL) of a webpage about a second topic that is of interest to the firstperson, wherein the second topic is determined to be of interest to thefirst person based upon an entry for the first person in acomputer-implemented knowledge graph and a second article thatreferences both the first person and the second topic; and uponreceiving a request for the first article from a computing device,causing the first article and a link to be concurrently displayed on adisplay, wherein the URL of the webpage is embedded in the link.
 2. Thecomputing system of claim 1, the acts further comprising: prior toobtaining the first article, executing a search over the knowledge graphbased upon an identifier for a profession; responsive to executing thesearch, identifying a plurality of persons referenced in the knowledgegraph, wherein each person in the plurality of persons belongs to theprofession, and further wherein the plurality of persons includes thefirst person; and for each person in the plurality of persons, obtaininga name of the person from an entry for the person in the knowledgegraph.
 3. The computing system of claim 2, wherein the profession is oneor more of: an actor; a politician; a social media influencer; or anathlete.
 4. The computing system of claim 2, the acts furthercomprising: subsequent to obtaining a name of the first person,identifying a plurality of articles that reference the first personbased upon the name of the first person, wherein the plurality ofarticles include the second article; and providing content of the secondarticle as input to a computer-implemented named-entity recognitionmodel, wherein the named-entity recognition model outputs an indicationthat the second topic referenced in the second article is of interest tothe first person based upon the content of the second article.
 5. Thecomputing system of claim 2, the acts further comprising: subsequent toobtaining the first article, determining that the first person isincluded in the plurality of persons based upon a name of the firstperson; and wherein the first article and the link are caused to beconcurrently displayed in accordance with the first person beingincluded in the plurality of persons.
 6. The computing system of claim4, the acts further comprising: subsequent to identifying that thesecond topic is of interest to the first person, obtaining the URL ofthe webpage that includes the information pertaining to the secondtopic; and storing the URL of the webpage in a data store.
 7. Thecomputing system of claim 1, the acts further comprising: obtaining aname of the first person from the first article; executing a search overthe knowledge graph based upon the name of the first person; identifyinga profession of the first person from the knowledge graph; anddetermining that the profession of the first person is included in aplurality of professions, wherein the first article and the link arecaused to be concurrently displayed in accordance with the profession ofthe first person being included in the plurality of professions.
 8. Thecomputing system of claim 1, wherein causing the first article and thelink to be concurrently displayed on the display comprises: insertingthe link into the first article; and transmitting the first article withthe link inserted therein to the computing device, wherein the firstarticle and the link are presented concurrently on the display by thecomputing device.
 9. The computing system of claim 1, wherein the secondtopic relates to one or more of: an athlete; a sports team; a sportsleague; a sports event; an entertainment event; a performer; an actor; atelevision show; a director; or a politician.
 10. The computing systemof claim 1, wherein the plurality of entities referenced in the firstarticle further include a first entity, wherein the first person isdetermined to be the dominant entity of the first article in accordancewith the number of references to the first person within the firstarticle exceeding a number of references to the first entity within thefirst article.
 11. The computing system of claim 1, wherein thereferences to the first person within the first article include one ormore of: a full name of the first person; a first name of the firstperson; a last name of the first person; a subject pronoun referring tothe first person; an object pronoun referring to the first person; apossessive adjective referring to the first person; a possessive pronounreferring to the first person; or a reflexive pronoun referring to thefirst person.
 12. A method executed by a processor of a computingsystem, the method comprising: obtaining a computer-readable firstarticle about a first topic, wherein the first article references aplurality of entities, and further wherein the plurality of entitiesinclude a person; identifying the person as a dominant entity of thefirst article based upon a number of references to the person within thefirst article; determining whether the first article expresses negativesentiment based upon content of the first article; in accordance with adetermination that the first article does not express negativesentiment: retrieving a uniform resource locator (URL) of a webpageabout a second topic that is of interest to the person, wherein thesecond topic is determined to be of interest to the person based upon anentry for the person in a computer-implemented knowledge graph and asecond article that references both the person and the second topic; andupon receiving a request for the first article from a computing device,causing the first article and a link to be concurrently displayed on adisplay, wherein the URL of the webpage is embedded in the link; and inaccordance with a determination that the first article expressesnegative sentiment: upon receiving the request for the first articlefrom the computing device, causing the first article to be displayed onthe display without concurrently displaying the link.
 13. The method ofclaim 12, further comprising: prior to obtaining the first article,executing a search over the knowledge graph based upon an identifier fora profession; responsive to executing the search, identifying aplurality of persons referenced in the knowledge graph based upon searchresults for the search, wherein each person in the plurality of personsbelongs to the profession, and further wherein the plurality of personsincludes the person; and obtaining names of each person in the pluralityof persons from entries for each person in the knowledge graph.
 14. Themethod of claim 13, further comprising: subsequent to obtaining thenames of each person in the plurality of persons, identifying aplurality of articles that reference the person based upon a name of theperson, wherein the plurality of articles include the second articlethat references both the person and the second topic; providing contentof the second article as input to a computer-implemented named-entityrecognition model, wherein the named-entity recognition model outputs anindication that the second topic referenced in the second article is ofinterest to the person based upon the content of the second article; andstoring entity interest data in computer-readable storage, wherein theentity interest data indicates that the second topic is of interest tothe person.
 15. The method of claim 14, wherein the named-entityrecognition model is a deep learning model.
 16. The method of claim 12,wherein a third topic is of interest to the person, the method furthercomprising: ranking the second topic and the third topic based uponranking criteria; and selecting the second topic based upon a rank ofthe second topic and a rank of the third topic, wherein the link isdisplayed on the display concurrently with the first article based uponthe second topic being selected.
 17. The method of claim 12, wherein thearticle is presented within a first region of a graphical user interface(GUI), wherein the link is presented within a second region of the GUI.18. A non-transitory computer-readable storage medium comprisinginstructions that, when executed by a processor of a computing system,cause the processor to perform acts comprising: obtaining acomputer-readable first article about a first topic, wherein the firstarticle references a plurality of entities, and further wherein theplurality of entities include a first person; identifying the firstperson referenced in the first article as a dominant entity of the firstarticle based upon a number of references to the first person within thefirst article; determining whether the first article expressesnon-negative sentiment based upon content of the first article; when thefirst article is determined to express non-negative sentiment,retrieving a uniform resource locator (URL) of a webpage about a secondtopic that is of interest to the first person, wherein the second topicis determined to be of interest to the first person based upon an entryfor the first person in a computer-implemented knowledge graph and asecond article that references both the first person and the secondtopic; and upon receiving a request for the first article from acomputing device, transmitting the first article and a link to thecomputing device for concurrent presentment on a display, wherein theURL of the webpage is embedded in the link.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein the link isdisplayed on the display as a question, wherein the question includes aname of the first person and a name of the second topic that is ofinterest to the first person.
 20. The non-transitory computer-readablestorage medium of claim 18, wherein the webpage includes URLs forwebpages, wherein each webpage in the webpages references both the firstperson and the second topic.